• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.135-145
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• 2019

Analysis of a long cycle or a trend of time series data based on a long-term observation would require comparability between data observed in the past and the present. In the present study, we proposed an approach to ensure the compatibility among the instruments used for the long-term observation, which would allow to secure continuity of the data. An open-path gas analyzer (Model LI-7500, LI-COR, Inc., USA) has been used for eddy covariance flux measurement in the Gwangneung deciduous forest for more than 10 years. The open-path gas analyzer was replaced by an enclosed-path gas analyzer (Model EC155, Campbell Scientific, Inc., USA) in July 2015. Before completely replacing the gas analyzer, the carbon dioxide ($CO_2$) and latent heat fluxes were collected using both gas analyzers simultaneously during a five-month period from August to December in 2015. It was found that the $CO_2$ fluxes were not significantly different between the gas analyzers under the condition that the daily mean temperature was higher than $0^{\circ}C$. However, the $CO_2$ flux measured by the open-path gas analyzer was negatively biased (from positive sign, i.e., carbon source, to 0 or negative sign, i.e., carbon neutral or sink) due to the instrument surface heating under the condition that the daily mean temperature was lower than $0^{\circ}C$. Despite applying the frequency response correction associated with tube attenuation of water vapor, the latent heat flux measured by the enclosed-path gas analyzer was on average 9% smaller than that measured by the open-path gas analyzer, which resulted in >20% difference of the sums over the study period. These results indicated that application of the additional air density correction would be needed due to the instrument heat and analysis of the long-term observational flux data would be facilitated by understanding the underestimation tendency of latent heat flux measurements by an enclosed-path gas analyzer.

• Journal of Bio-Environment Control
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• v.32 no.2
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• pp.148-156
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• 2023

This study was conducted in an indoor cultivation room and chamber where environmental control is possible to investigate the effect of temperature and irrigation interval on photosynthesis, growth and growth analysis of potted seedling cucumber. The light intensity (70 W·m^-2) and humidity (65%) were set to be the same. The experimental treatments were six combinations of three different temperatures, 15/10℃, 25/20℃, and 35/25℃, and two irrigation intervals, 100 mL per day (S) and 200 mL every 2 days (L). The treatments were named 15S, 15L, 25S, 25L, 35S, and 35L. Seedlings at 0.5 cm in height were planted in pots (volume:1 L) filled with sandy loam and treated for 21 days. Photosynthesis, transpiration rate and stomatal conductance at 14 days after treatment were highest in 25S. These were higher in S treatments with a shorter irrigation interval than L treatments. Total amount of irrigation water was supplied evenly at 2 L, but the soil moisture content was highest at 15S and lowest at 25S > 15L > 25L, 35S and 35L in that order. Humidity showed a similar trend at 15/10℃ (61.1%) and 25/20℃ (67.2%), but it was as high at 35/25℃ (80.5%). Cucumber growth (plant height, leaf length, leaf width, chlorophyll content, leaf area, fresh weight and dry weight) on day 21 was the highest in 25S. Growth parameters were higher in S with shorter irrigation intervals. Yellow symptom of leaf was occurred in 89.9% at 35S and 35L, where the temperature was high. Relative growth rate (RGR) and specific leaf weight (SLA) were high at 25/20℃ (25S, 25L), RGR tended to be high in the S treatment, and SLA in the L treatment. Water use efficiency (WUE) was high in the order of 25S, 25L > 15S > 15L, 35S, and 35L. As a result of the above, the growth and WUE were high at the temperature of 25/20℃.

• The KIPS Transactions:PartA
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• v.11A no.4
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• pp.243-250
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• 2004

Monitoring is used to see if a real-time system provides a service on time. Generally, monitoring for real-time focuses on investigating the current status of a real-time system. To support a stable performance of a real-time system, it should have not only a function to see the current status of real-time process but also a function to predict executions of real-time processes, however. The legacy prediction model has some limitation to apply it to a real-time monitoring. First, it performs a static prediction after a real-time process finished. Second, it needs a statistical pre-analysis before a prediction. Third, transition probability and data about clustering is not based on the current data. We propose the execution prediction model based on learning algorithm to solve these problems and apply it to real-time monitoring. This model gets rid of unnecessary pre-processing and supports a precise prediction based on current data. In addition, this supports multi-level prediction by a trend analysis of past execution data. Most of all, We designed the model to support dynamic prediction which is performed within a real-time process' execution. The results from some experiments show that the judgment accuracy is greater than 80% if the size of a training set is set to over 10, and, in the case of the multi-level prediction, that the prediction difference of the multi-level prediction is minimized if the number of execution is bigger than the size of a training set. The execution prediction model proposed in this model has some limitation that the model used the most simplest learning algorithm and that it didn't consider the multi-regional space model managing CPU, memory and I/O data. The execution prediction model based on a learning algorithm proposed in this paper is used in some areas related to real-time monitoring and control.

• Korean Journal of Environmental Biology
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• v.32 no.4
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• pp.286-296
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• 2014

In this study, we investigated the macrobenthic community structure and spatiotemporal variations in Uljin Marine Ranching area, East Sea of Korea. Macrobenthos were collected using a modified van Veen grab sampler from April to September 2013. Total number of species sampled was 345 and mean density was 5,797 ind. $m^{-2}$, both of which were dominated by the polychaetes. The most dominant species were Spiophanes bombyx (53.64%), followed by Magelona sp.1 (6.96%), Cadella semitorta (2.73%), Lumbrineris longifolia (2.16%) and Alvenius ojianus (2.08%). Cluster analysis and nMDS ordination analysis based on the Bray-Curtis similarity identified 2 station groups. The group 1 (station 2, 3, 5, 6, 8 and 9) was characterized by high abundance of the polychaetes Magelona sp.1, Lumbrineris longifolia, Scoloplos armiger, Praxillella affinis, Maldane cristata and the bivalve Alvenius ojianus, with fine sediment above 30m water depth. On the other hand, the group 2 (station 1, 4, 7 and 10) was numerically dominated by the polychaete Lumbrineriopsis sp. and the bivalve Cadella semitorta, with coarse sediment below 5m water depth. Collectively, the macrobenthic community structure showed a distinct spatial trend, which seemed to be related to the water depth and sediment composition.

• Journal of Mushroom
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• v.15 no.1
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• pp.45-53
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• 2017

Profitability of farmers has decreased mainly owing to low price while the gross amount of mushroom production has increased continuously in South Korea. In this regard, analyzing patterns of mushroom consumption is believed to be meaningful. We used a panel data set consisting of 667 families, from 2010 to 2015. Based on the panel data, mushroom consumption patterns of people living in city areas were examined. Multiple descriptive analysis methods and frequency analysis approaches were adopted in this study in terms of time and space dimensions, demographic properties, and purchase behaviors. The findings of this studyshow that mushroom purchase is highly dependent on seasonal events, which implies that the product consumption timing is predictable. In addition, yearly purchase amount patterns reflect that superstores have become the major mushroomtrading venues. This directly supports the need to establish supply chain capabilities for mushroom farmers so that they gain more bargaining power against enterprise-type groceries. Finally, functional features of mushroom can be linked with marketing promotion because purchase patterns demonstrate potential needs for healthcare food in mushroom categories. Based on the analyzed patterns, this paper provides insightful implications for policy makers who want to promote mushroom consumption.

• Journal of Broadcast Engineering
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• v.15 no.4
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• pp.563-581
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• 2010

In this paper, we perform subjective visual quality assessments on UHD video for UHD TV services and analyze the assessment results. Demands for video services have been increased with availabilities of DTV, Internet and personal media equipments. With this trend, the demands for high definition video have also been increasing. Currently, 2K-HD ($1920{\times}1080$) video have been widely consumed over DTV, DVD, digital camcoders, security cameras and other multimedia terminals in various types, and recently digital cinema contents of 4K-UHD($3840{\times}2160$) have been popularly produced and the cameras, beam projects, display panels that support for 4K-UHD video start to come out into multimedia markets. Also it is expected that 4K-UHD service will appear soon in broadcasting and telecommunications environments. Therefore, in this paper, subjective assessments of visual quality on resolutions, color formats, frame rates and compression rates have been carried to provide basis information for standardization of signal specification of UHD video and viewing environments for future UHDTV. As the analysis on the assessments, UHD video exhibits better subjective visual quality than HD by the evaluators. Also, the 4K-UHD test sequences in YUV444 shows better subjective visual quality than the 4K-UHD test sequences in YUV422 and YUV420, but there is little perceptual difference on 4K-UHD test sequences between YUV422 and YUV420 formats. For the comparison between different frame rates, 4K-UHD test sequences of 60fps gives better subjective visual quality than those of 30fps. For bit-depth comparison, HD test sequences in 10-bit depth were little differentiated from those in 8-bit depth in subject visual quality assessment. Lastly, the larger the PSNR values of the reconstructed 4K-UHD test sequences are, the higher the subjective visual quality is. Against the viewing distances, the differences among encoded 4K-UHD test sequences were less distinguished in longer distances from the display.

• Journal of Korean Ophthalmic Optics Society
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• v.13 no.4
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• pp.65-73
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• 2008

Purpose: The purpose of this study is to investigate the effect of age in the response to long-term overnight orthokeratology (OK) lens wearing. Methods: Among volunteers, ninety-five healthy subjects who had no eye diseases and could wear OK contact lens at least for 8 hours every day were divided into three groups children, youngsters and young adults. Unaided logMAR visual acuity, refractive error, apical corneal radius, corneal asphericity and central corneal thickness were measured with different period; before and after one day, one week, two weeks, one month, three months and six months of OK lens wear. Paired student t-test, ANOVA analysis and Pearson correlation were used with a critical p value of 0.05 for statistical analysis. Results: All groups showed statistically significant (p<0.001) improvement in unaided visual acuity, a trend for flattening in the apical corneal radius, decrease in central corneal thickness and less prolate after OK lens wear. The child group showed significantly rapid change (p<0.001) in visual acuity, and apical corneal radius showed that they reached the targeted refractive change earlier compared with youngster and adult groups. The visual effect of OK lens was significantly related with the change in central corneal thickness after long-term OK lens wear, especially in child and youngster group, and central corneal thickness were highly correlated with the targeted refractive change. Conclusions: Visual acuity change is statistically correlated with the central corneal thickness change, which is highly correlated with targeted refractive change in the long-term orthokeratology and younger lens wearers showed a rapid response to OK lens wear, suggesting a reduced epithelial response with increasing age. The results found this study extends our understanding and development in the long-term orthokeratology.

• Journal of Global Scholars of Marketing Science
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• v.18 no.3
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• pp.59-88
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• 2008

Digital Convergence means integration between industry, technology, and contents, and in marketing, it usually comes with creation of new types of product and service under the base of digital technology as digitalization progress in electro-communication industries including telecommunication, home appliance, and computer industries. One can see digital convergence not only in instruments such as PC, AV appliances, cellular phone, but also in contents, network, service that are required in production, modification, distribution, re-production of information. Convergence in contents started around 1990. Convergence in network and service begins as broadcasting and telecommunication integrates and DMB(digital multimedia broadcasting), born in May, 2005 is the symbolic icon in this trend. There are some positive and negative expectations about DMB. The reason why two opposite expectations exist is that DMB does not come out from customer's need but from technology development. Therefore, customers might have hard time to interpret the real meaning of DMB. Time is quite critical to a high tech product, like DMB because another product with same function from different technology can replace the existing product within short period of time. If DMB does not positioning well to customer's mind quickly, another products like Wibro, IPTV, or HSPDA could replace it before it even spreads out. Therefore, positioning strategy is critical for success of DMB product. To make correct positioning strategy, one needs to understand how consumer interprets DMB and how consumer's interpretation can be changed via communication strategy. In this study, we try to investigate how consumer perceives a new product, like DMB and how AD strategy change consumer's perception. More specifically, the paper segment consumers into sub-groups based on their DMB perceptions and compare their characteristics in order to understand how they perceive DMB. And, expose them different printed ADs that have messages guiding consumer think DMB in specific ways, either cellular phone or personal TV. Research Question 1: Segment consumers according to perceptions about DMB and compare characteristics of segmentations. Research Question 2: Compare perceptions about DMB after AD that induces categorization of DMB in direction for each segment. If one understand and predict a direction in which consumer perceive a new product, firm can select target customers easily. We segment consumers according to their perception and analyze characteristics in order to find some variables that can influence perceptions, like prior experience, usage, or habit. And then, marketing people can use this variables to identify target customers and predict their perceptions. If one knows how customer's perception is changed via AD message, communication strategy could be constructed properly. Specially, information from segmented customers helps to develop efficient AD strategy for segment who has prior perception. Research framework consists of two measurements and one treatment, O1 X O2. First observation is for collecting information about consumer's perception and their characteristics. Based on first observation, the paper segment consumers into two groups, one group perceives DMB similar to Cellular phone and the other group perceives DMB similar to TV. And compare characteristics of two segments in order to find reason why they perceive DMB differently. Next, we expose two kinds of AD to subjects. One AD describes DMB as Cellular phone and the other Ad describes DMB as personal TV. When two ADs are exposed to subjects, consumers don't know their prior perception of DMB, in other words, which subject belongs 'similar-to-Cellular phone' segment or 'similar-to-TV' segment? However, we analyze the AD's effect differently for each segment. In research design, final observation is for investigating AD effect. Perception before AD is compared with perception after AD. Comparisons are made for each segment and for each AD. For the segment who perceives DMB similar to TV, AD that describes DMB as cellular phone could change the prior perception. And AD that describes DMB as personal TV, could enforce the prior perception. For data collection, subjects are selected from undergraduate students because they have basic knowledge about most digital equipments and have open attitude about a new product and media. Total number of subjects is 240. In order to measure perception about DMB, we use indirect measurement, comparison with other similar digital products. To select similar digital products, we pre-survey students and then finally select PDA, Car-TV, Cellular Phone, MP3 player, TV, and PSP. Quasi experiment is done at several classes under instructor's allowance. After brief introduction, prior knowledge, awareness, and usage about DMB as well as other digital instruments is asked and their similarities and perceived characteristics are measured. And then, two kinds of manipulated color-printed AD are distributed and similarities and perceived characteristics for DMB are re-measured. Finally purchase intension, AD attitude, manipulation check, and demographic variables are asked. Subjects are given small gift for participation. Stimuli are color-printed advertising. Their actual size is A4 and made after several pre-test from AD professionals and students. As results, consumers are segmented into two subgroups based on their perceptions of DMB. Similarity measure between DMB and cellular phone and similarity measure between DMB and TV are used to classify consumers. If subject whose first measure is less than the second measure, she is classified into segment A and segment A is characterized as they perceive DMB like TV. Otherwise, they are classified as segment B, who perceives DMB like cellular phone. Discriminant analysis on these groups with their characteristics of usage and attitude shows that Segment A knows much about DMB and uses a lot of digital instrument. Segment B, who thinks DMB as cellular phone doesn't know well about DMB and not familiar with other digital instruments. So, consumers with higher knowledge perceive DMB similar to TV because launching DMB advertising lead consumer think DMB as TV. Consumers with less interest on digital products don't know well about DMB AD and then think DMB as cellular phone. In order to investigate perceptions of DMB as well as other digital instruments, we apply Proxscal analysis, Multidimensional Scaling technique at SPSS statistical package. At first step, subjects are presented 21 pairs of 7 digital instruments and evaluate similarity judgments on 7 point scale. And for each segment, their similarity judgments are averaged and similarity matrix is made. Secondly, Proxscal analysis of segment A and B are done. At third stage, get similarity judgment between DMB and other digital instruments after AD exposure. Lastly, similarity judgments of group A-1, A-2, B-1, and B-2 are named as 'after DMB' and put them into matrix made at the first stage. Then apply Proxscal analysis on these matrixes and check the positional difference of DMB and after DMB. The results show that map of segment A, who perceives DMB similar as TV, shows that DMB position closer to TV than to Cellular phone as expected. Map of segment B, who perceive DMB similar as cellular phone shows that DMB position closer to Cellular phone than to TV as expected. Stress value and R-square is acceptable. And, change results after stimuli, manipulated Advertising show that AD makes DMB perception bent toward Cellular phone when Cellular phone-like AD is exposed, and that DMB positioning move towards Car-TV which is more personalized one when TV-like AD is exposed. It is true for both segment, A and B, consistently. Furthermore, the paper apply correspondence analysis to the same data and find almost the same results. The paper answers two main research questions. The first one is that perception about a new product is made mainly from prior experience. And the second one is that AD is effective in changing and enforcing perception. In addition to above, we extend perception change to purchase intention. Purchase intention is high when AD enforces original perception. AD that shows DMB like TV makes worst intention. This paper has limitations and issues to be pursed in near future. Methodologically, current methodology can't provide statistical test on the perceptual change, since classical MDS models, like Proxscal and correspondence analysis are not probability models. So, a new probability MDS model for testing hypothesis about configuration needs to be developed. Next, advertising message needs to be developed more rigorously from theoretical and managerial perspective. Also experimental procedure could be improved for more realistic data collection. For example, web-based experiment and real product stimuli and multimedia presentation could be employed. Or, one can display products together in simulated shop. In addition, demand and social desirability threats of internal validity could influence on the results. In order to handle the threats, results of the model-intended advertising and other "pseudo" advertising could be compared. Furthermore, one can try various level of innovativeness in order to check whether it make any different results (cf. Moon 2006). In addition, if one can create hypothetical product that is really innovative and new for research, it helps to make a vacant impression status and then to study how to form impression in more rigorous way.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.45-68
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• 2022

The central and local governments of the Republic of Korea provided information necessary for disaster response through wireless emergency alerts (WEAs) in order to overcome the pandemic situation in which COVID-19 rapidly spreads. Among all channels for delivering disaster information, wireless emergency alert is the most efficient, and since it adopts the CBS(Cell Broadcast Service) method that broadcasts directly to the mobile phone, it has the advantage of being able to easily access disaster information through the mobile phone without the effort of searching. In this study, the characteristics of wireless emergency alerts sent to Seoul during the past year and one month (January 2020 to January 2021) were derived through various text mining methodologies, and various types of information contained in wireless emergency alerts were analyzed. In addition, it was confirmed through the population mobility by age in the districts of Seoul that what kind of influence it had on the movement behavior of people. After going through the process of classifying key words and information included in each character, text analysis was performed so that individual sent characters can be used as an analysis unit by applying a document cluster analysis technique based on the included words. The number of WEAs sent to the Seoul has grown dramatically since the spread of Covid-19. In January 2020, only 10 WEAs were sent to the Seoul, but the number of the WEAs increased 5 times in March, and 7.7 times over the previous months. Since the basic, regional local government were authorized to send wireless emergency alerts independently, the sending behavior of related to wireless emergency alerts are different for each local government. Although most of the basic local governments increased the transmission of WEAs as the number of confirmed cases of Covid-19 increases, the trend of the increase in WEAs according to the increase in the number of confirmed cases of Covid-19 was different by region. By using structured econometric model, the effect of disaster information included in wireless emergency alerts on population mobility was measured by dividing it into baseline effect and accumulating effect. Six types of disaster information, including date, order, online URL, symptom, location, normative guidance, were identified in WEAs and analyzed through econometric modelling. It was confirmed that the types of information that significantly change population mobility by age are different. Population mobility of people in their 60s and 70s decreased when wireless emergency alerts included information related to date and order. As date and order information is appeared in WEAs when they intend to give information about Covid-19 confirmed cases, these results show that the population mobility of higher ages decreased as they reacted to the messages reporting of confirmed cases of Covid-19. Online information (URL) decreased the population mobility of in their 20s, and information related to symptoms reduced the population mobility of people in their 30s. On the other hand, it was confirmed that normative words that including the meaning of encouraging compliance with quarantine policies did not cause significant changes in the population mobility of all ages. This means that only meaningful information which is useful for disaster response should be included in the wireless emergency alerts. Repeated sending of wireless emergency alerts reduces the magnitude of the impact of disaster information on population mobility. It proves indirectly that under the prolonged pandemic, people started to feel tired of getting repetitive WEAs with similar content and started to react less. In order to effectively use WEAs for quarantine and overcoming disaster situations, it is necessary to reduce the fatigue of the people who receive WEA by sending them only in necessary situations, and to raise awareness of WEAs.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.